


import logging
from pathlib import Path

import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json


def       UpperCAmelCase_					(   UpperCAmelCase__	):
					lowercase_					=      filter(lambda UpperCAmelCase__	: p.requires_grad    ,				model.parameters()	)
					lowercase_					=      sum([np.prod(p.size()	) for p in model_parameters]	)
					return params


a       	=  logging.getLogger(__name__)


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__	):

					if metric == "rouge2":
										lowercase_					=      """{val_avg_rouge2:.4f}-{step_count}"""
					elif metric == "bleu":
										lowercase_					=      """{val_avg_bleu:.4f}-{step_count}"""
					elif metric == "em":
										lowercase_					=      """{val_avg_em:.4f}-{step_count}"""
					else:
										raise NotImplementedError(
										    F'''seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this'''
										    """ function."""	)

					lowercase_					=      ModelCheckpoint(
					    dirpath=a__    ,				filename=a__    ,				monitor=F'''val_{metric}'''    ,				mode="""max"""    ,				save_top_k=3    ,				every_n_epochs=1    ,				)
					return checkpoint_callback


def       UpperCAmelCase_					(   UpperCAmelCase__    ,				UpperCAmelCase__	):
					return EarlyStopping(
					    monitor=F'''val_{metric}'''    ,				mode="""min""" if """loss""" in metric else """max"""    ,				patience=a__    ,				verbose=a__    ,				)







class 							UpperCamelCase__ (						pl.Callback ):

					def 	UpperCAmelCase__							(  self							: Dict				,       UpperCamelCase__							: List[Any]				,       UpperCamelCase__							: int							):






										'''simple docstring'''






										lowercase_					=      {F'''lr_group_{i}''': param["""lr"""] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups							)}
										pl_module.logger.log_metrics(lowercase_							)

					@rank_zero_only
					def 	UpperCAmelCase__							(  self							: List[Any]				,       UpperCamelCase__							: pl.Trainer				,       UpperCamelCase__							: pl.LightningModule				,       UpperCamelCase__							: str				,       UpperCamelCase__							: Any=True							):






										'''simple docstring'''






										logger.info(F'''***** {type_path} results at step {trainer.global_step:05d} *****'''							)
										lowercase_					=      trainer.callback_metrics
										trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ["""log""", """progress_bar""", """preds"""]}							)
										# Log results
										lowercase_					=      Path(pl_module.hparams.output_dir							)
										if type_path == "test":
															lowercase_					=      od / """test_results.txt"""
															lowercase_					=      od / """test_generations.txt"""
										else:
															# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
															# If people want this it will be easy enough to add back.
															lowercase_					=      od / F'''{type_path}_results/{trainer.global_step:05d}.txt'''
															lowercase_					=      od / F'''{type_path}_generations/{trainer.global_step:05d}.txt'''
															results_file.parent.mkdir(exist_ok=lowercase_							)
															generations_file.parent.mkdir(exist_ok=lowercase_							)
										with open(lowercase_				,       """a+"""							) as writer:
															for key in sorted(lowercase_							):
																				if key in ["log", "progress_bar", "preds"]:
																									continue
																				lowercase_					=      metrics[key]
																				if isinstance(lowercase_				,       torch.Tensor							):
																									lowercase_					=      val.item()
																				lowercase_					=      F'''{key}: {val:.6f}\n'''
																				writer.write(lowercase_							)

										if not save_generations:
															return

										if "preds" in metrics:
															lowercase_					=      """\n""".join(metrics["""preds"""]							)
															generations_file.open("""w+"""							).write(lowercase_							)

					@rank_zero_only
					def 	UpperCAmelCase__							(  self							: int				,       UpperCamelCase__							: Tuple				,       UpperCamelCase__							: Optional[Any]							):






										'''simple docstring'''






										try:
															lowercase_					=      pl_module.model.model.num_parameters()
										except AttributeError:
															lowercase_					=      pl_module.model.num_parameters()

										lowercase_					=      count_trainable_parameters(lowercase_							)
										# mp stands for million parameters
										trainer.logger.log_metrics({"""n_params""": npars, """mp""": npars / 1e6, """grad_mp""": n_trainable_pars / 1e6}							)

					@rank_zero_only
					def 	UpperCAmelCase__							(  self							: Any				,       UpperCamelCase__							: pl.Trainer				,       UpperCamelCase__							: pl.LightningModule							):






										'''simple docstring'''






										save_json(pl_module.metrics				,       pl_module.metrics_save_path							)
										return self._write_logs(lowercase_				,       lowercase_				,       """test"""							)







					@rank_zero_only
					def 	UpperCAmelCase__							(  self							: Optional[int]				,       UpperCamelCase__							: pl.Trainer				,       UpperCamelCase__							: Optional[int]							):






										'''simple docstring'''






										save_json(pl_module.metrics				,       pl_module.metrics_save_path							)
										# Uncommenting this will save val generations
										# return self._write_logs(trainer, pl_module, "valid")



def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  if not isinstance(a__    ,     a__ ) or number < 0:
    raise ValueError("Input must be a non-negative integer" )

  _UpperCamelCase           =    0
  while number:
    # This way we arrive at next set bit (next 1) instead of looping
    # through each bit and checking for 1s hence the
    # loop won't run 32 times it will only run the number of `1` times
    number &= number - 1
    count += 1
  return count


if __name__ == "__main__":
   import doctest

   doctest.testmod()




